1. Field of the Invention
Aspects of the present invention relate to a digital content recommendation system, and more particularly, to a method of and apparatus for constructing a user profile by using a content tag, and a method of recommending digital content by using the generated user profile.
2. Description of the Related Art
In a digital content recommendation system, by comparing profiles of users, a user can identify other user's tastes and recommend appropriate digital content. The user profiles indicate the preferences of the users for digital content, and the users represent their respective preferences according to a preference value of the digital content.
A user profile can be constructed with basic data of a user and digital content consumption information. The basic data includes age, sex, occupation, location, name, and so on. In the digital content consumption information, a preference value may be specified for each digital content file, or for metadata of a digital content file.
FIG. 1 shows an example of a user profile constructed by specifying a preference value for digital content according to conventional technology. Digital content consumed by a user and a preference value according to the digital content consumption are specified. The preference value can be expressed by preference and non-preference values such as Good and Bad, or by numbers indicating the degrees of preference.
FIG. 2 shows an example of a user profile constructed by specifying a preference value for metadata according to conventional technology. Metadata included in digital content and a preference value for the digital content are included in a user profile. The metadata includes items such as genres, actors, and titles. The items of the metadata are determined by a digital content producer. Accordingly, the metadata would have standardized items.
In a conventional digital content recommendation system, a user profile is generated as explained below, and by comparing generated user profiles, appropriate digital content is recommended to a user (who is referred to as an active user).
1. Generation of a user profile: If a user consumes digital content, feedback according to the digital content consumption is stored together with the name of the digital content as a profile.
2. Comparison of user profiles: The user profile is compared with a profile of another user. The preference of each user is represented by the digital content file consumed by the user and a preference value according to the digital content consumption.
For example, in the example shown in FIG. 1, there is no digital content file which user A and user B have in common, and user A and user B are classified as users having different preferences accordingly. It is impossible to recommend digital content between users having different preferences.
In the comparison method using profiles and metadata, only metadata items specified in advance by a content producer are compared. Accordingly, the method is characterized in that only standardized comparison is possible.
The conventional technology described above has various problems. If the amount of digital content files increases, the probability that digital content that is commonly used by multiple users does not exist increases. This is referred to as sparsity. Further, even when metadata is used, there is a limitation in that comparison is performed only within the scope of standardized metadata items. Due to this limitation, it is difficult to reflect a variety of preferences of users. Also, the metadata defined by the content producer is not expanded by other users, and it is difficult to reflect descriptive preferences of users.